{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a63c0e19-3902-44ee-9b02-ba7ecfdb5268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "couch lizard tractor plain sweet_pepper camel plain keyboard whale lizard cockroach orchid television rocket house apple cockroach palm_tree elephant sea   forest ray   baby  orchid pickup_truck can   leopard bed   butterfly tank  raccoon clock\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from useful_tools import *\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "from chao_kk import *\n",
    "from torch.optim import lr_scheduler\n",
    "\n",
    "\n",
    "batch_size = 32\n",
    "trainloader, testloader, classes = get_dataLoader_RandAugment(batch_size)\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = next(dataiter)\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0a20afd9-e695-4f86-b63b-3c0806770d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def padding_size(dilation, kernel_size):\n",
    "  return dilation * (kernel_size - 1) // 2\n",
    "\n",
    "def conv3x3(in_channel, out_channel, stride, dilation = 1):\n",
    "  return nn.Conv2d(in_channel, out_channel, kernel_size = 3, stride = stride, padding = padding_size(dilation, 3), bias = False, dilation = dilation)\n",
    "\n",
    "def conv1x1(in_channel, out_channel, stride = 1, dilation = 1):\n",
    "  return nn.Conv2d(in_channel, out_channel, kernel_size = 1, stride = stride, padding = 0, bias = False, dilation = dilation)\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4436c076-1495-4759-bba9-802649dee737",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, num_layers, dropout=0.1):\n",
    "        super(TransformerEncoder, self).__init__()\n",
    "        self.encoder_layer = nn.TransformerEncoderLayer(d_model=embed_dim, nhead=num_heads, dropout=dropout)\n",
    "        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)\n",
    "        \n",
    "    def forward(self, src):\n",
    "        # src shape: (S, N, E) where S is the source sequence length, N is the batch size, and E is the feature number\n",
    "        output = self.transformer_encoder(src)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8a1eb695-87a9-4d22-a3f7-b19a22baf1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResBlock(nn.Module):\n",
    "  def __init__(self, in_channel, out_channel, stride, downsample = None, dilation = 1):\n",
    "    super(ResBlock, self).__init__()\n",
    "    self.conv1 = conv3x3(in_channel, out_channel, stride, dilation)\n",
    "    self.bn1 = nn.BatchNorm2d(out_channel)\n",
    "    self.conv2 = conv3x3(out_channel, out_channel, 1, dilation)\n",
    "    self.bn2 = nn.BatchNorm2d(out_channel)\n",
    "    self.relu = nn.ReLU()\n",
    "    self.downsample = downsample\n",
    "\n",
    "  def forward(self, x):\n",
    "    shortcut = x\n",
    "    x = self.conv1(x)\n",
    "    x = self.bn1(x)\n",
    "    x = self.relu(x)\n",
    "    x = self.conv2(x)\n",
    "    x = self.bn2(x)\n",
    "    if self.downsample != None:\n",
    "      shortcut = self.downsample(shortcut)\n",
    "    x += shortcut\n",
    "    return x\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "  def __init__(self, classes, base_channel = 64, embed_dim=512, num_heads=8, num_layers=6):\n",
    "    super(ResNet, self).__init__()\n",
    "    # 老实说，我怕64、128……512通道实在是太慢了，通过base_channel来调节通道。感觉可以设置为16\n",
    "    self.base_channel = base_channel\n",
    "    self.conv1 = conv3x3(3, base_channel, 1)\n",
    "    self.bn = nn.BatchNorm2d(base_channel)\n",
    "    self.relu = nn.ReLU()\n",
    "    self.max_pool = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
    "    self.conv2_x = self.init_ResBlock(base_channel * 1, base_channel * 1, stride = 1, block_num = 2, dilation = 1)\n",
    "    self.conv3_x = self.init_ResBlock(base_channel * 1, base_channel * 2, stride = 2, block_num = 2, dilation = 1)\n",
    "    self.conv4_x = self.init_ResBlock(base_channel * 2, base_channel * 4, stride = 2, block_num = 2, dilation = 1)\n",
    "    self.conv5_x = self.init_ResBlock(base_channel * 4, base_channel * 8, stride = 2, block_num = 2, dilation = 1)\n",
    "\n",
    "    embed_dim = base_channel * 8\n",
    "    self.transformer = TransformerEncoder(embed_dim, num_heads, num_layers)\n",
    "    self.fc1 = nn.Linear(embed_dim, embed_dim // 2)\n",
    "    self.fc2 = nn.Linear(embed_dim // 2, embed_dim // 4)\n",
    "    self.fc3 = nn.Linear(embed_dim // 4, len(classes))\n",
    "    self.pool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "    self.dropout = nn.Dropout(0.05)\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.conv1(x)\n",
    "    x = self.bn(x)\n",
    "    x = self.relu(x)\n",
    "    x = self.conv2_x(x)\n",
    "    x = self.conv3_x(x)\n",
    "    x = self.conv4_x(x)\n",
    "    x = self.conv5_x(x)\n",
    "\n",
    "    #print(x.shape) torch.Size([32, 512, 4, 4])\n",
    "    x = self.pool(x)  # shape: (batch_size, embed_dim, 1, 1)\n",
    "    x = x.squeeze(2).squeeze(2)  # shape: (batch_size, embed_dim)\n",
    "    # Prepare data for Transformer: (sequence_length, batch_size, embed_dim)\n",
    "    x = x.unsqueeze(0)  # shape: (1, batch_size, embed_dim)\n",
    "    # Transformer Encoder\n",
    "    x = self.transformer(x)  # shape: (sequence_length, batch_size, embed_dim)\n",
    "    # Remove the sequence dimension\n",
    "    x = x.squeeze(0)  # shape: (batch_size, embed_dim)\n",
    "    # Final classification layer\n",
    "    x = self.fc1(x) \n",
    "    x = self.relu(x)\n",
    "    x = self.fc2(x) \n",
    "    x = self.relu(x)\n",
    "    x = self.fc3(x) \n",
    "    x = self.dropout(x)\n",
    "    return x\n",
    "\n",
    "  def init_ResBlock(self, in_channel, out_channel, stride, block_num, dilation):\n",
    "    downsample = None #这个好东西是用来把特征图宽高缩小的\n",
    "    if stride != 1 or in_channel != out_channel:\n",
    "      # 对于3*3的padding为1的卷积核，如果stride为1的话卷完后特征图宽高是不变的。\n",
    "      # 如果stride是2的话，那么宽高会变成原来的一半。\n",
    "      # 计算：out = (in/2 - 1/2)向下取整 + 1， 也就是in/2 - 1 + 1 = in / 2\n",
    "      downsample = nn.Sequential(\n",
    "                conv3x3(in_channel, out_channel, stride=stride),\n",
    "                nn.BatchNorm2d(out_channel)\n",
    "                )\n",
    "    return nn.Sequential(*[\n",
    "        ResBlock(in_channel, out_channel, stride, downsample = downsample, dilation = dilation) if i == 0 else\n",
    "        ResBlock(out_channel, out_channel, 1, dilation = dilation)\n",
    "        for i in range(block_num)\n",
    "    ])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1fc55884-0a41-46af-bbb8-bcc55be86d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "net = ResNet(classes = classes, base_channel = 128).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "#optimizer = optim.Adam(net.parameters(), lr=0.001)\n",
    "print(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "48493633-6b18-408a-a0cd-b770da9f40b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.1602 Top-1 准确率: 0.0720 Top-5 准确率: 0.2367\n",
      "val 损失: 3.9115 Top-1 准确率: 0.0953 Top-5 准确率: 0.3022\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.4867 Top-1 准确率: 0.1659 Top-5 准确率: 0.4344\n",
      "val 损失: 3.3016 Top-1 准确率: 0.1867 Top-5 准确率: 0.4683\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.0211 Top-1 准确率: 0.2504 Top-5 准确率: 0.5578\n",
      "val 损失: 2.8580 Top-1 准确率: 0.2689 Top-5 准确率: 0.5942\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7125 Top-1 准确率: 0.3183 Top-5 准确率: 0.6272\n",
      "val 损失: 2.6186 Top-1 准确率: 0.3233 Top-5 准确率: 0.6444\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4738 Top-1 准确率: 0.3653 Top-5 准确率: 0.6772\n",
      "val 损失: 2.4878 Top-1 准确率: 0.3540 Top-5 准确率: 0.6754\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2973 Top-1 准确率: 0.4030 Top-5 准确率: 0.7143\n",
      "val 损失: 2.2658 Top-1 准确率: 0.4031 Top-5 准确率: 0.7154\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1402 Top-1 准确率: 0.4402 Top-5 准确率: 0.7446\n",
      "val 损失: 2.0111 Top-1 准确率: 0.4579 Top-5 准确率: 0.7682\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0218 Top-1 准确率: 0.4679 Top-5 准确率: 0.7661\n",
      "val 损失: 1.9531 Top-1 准确率: 0.4740 Top-5 准确率: 0.7742\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9051 Top-1 准确率: 0.4945 Top-5 准确率: 0.7888\n",
      "val 损失: 1.9021 Top-1 准确率: 0.4791 Top-5 准确率: 0.7886\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8028 Top-1 准确率: 0.5183 Top-5 准确率: 0.8063\n",
      "val 损失: 1.7699 Top-1 准确率: 0.5219 Top-5 准确率: 0.8085\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7094 Top-1 准确率: 0.5432 Top-5 准确率: 0.8225\n",
      "val 损失: 1.7599 Top-1 准确率: 0.5275 Top-5 准确率: 0.8113\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6246 Top-1 准确率: 0.5593 Top-5 准确率: 0.8366\n",
      "val 损失: 1.7531 Top-1 准确率: 0.5238 Top-5 准确率: 0.8137\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5500 Top-1 准确率: 0.5828 Top-5 准确率: 0.8495\n",
      "val 损失: 1.8099 Top-1 准确率: 0.5124 Top-5 准确率: 0.8086\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4812 Top-1 准确率: 0.5964 Top-5 准确率: 0.8597\n",
      "val 损失: 1.6750 Top-1 准确率: 0.5503 Top-5 准确率: 0.8264\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4033 Top-1 准确率: 0.6173 Top-5 准确率: 0.8729\n",
      "val 损失: 1.6439 Top-1 准确率: 0.5587 Top-5 准确率: 0.8332\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3445 Top-1 准确率: 0.6318 Top-5 准确率: 0.8812\n",
      "val 损失: 1.6407 Top-1 准确率: 0.5611 Top-5 准确率: 0.8356\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2872 Top-1 准确率: 0.6476 Top-5 准确率: 0.8905\n",
      "val 损失: 1.6955 Top-1 准确率: 0.5576 Top-5 准确率: 0.8283\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2196 Top-1 准确率: 0.6629 Top-5 准确率: 0.8992\n",
      "val 损失: 1.7017 Top-1 准确率: 0.5595 Top-5 准确率: 0.8306\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1819 Top-1 准确率: 0.6704 Top-5 准确率: 0.9049\n",
      "val 损失: 1.6411 Top-1 准确率: 0.5751 Top-5 准确率: 0.8426\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1125 Top-1 准确率: 0.6921 Top-5 准确率: 0.9152\n",
      "val 损失: 1.6888 Top-1 准确率: 0.5799 Top-5 准确率: 0.8350\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0702 Top-1 准确率: 0.7010 Top-5 准确率: 0.9202\n",
      "val 损失: 1.6882 Top-1 准确率: 0.5678 Top-5 准确率: 0.8378\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0221 Top-1 准确率: 0.7144 Top-5 准确率: 0.9257\n",
      "val 损失: 1.6176 Top-1 准确率: 0.5902 Top-5 准确率: 0.8493\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9802 Top-1 准确率: 0.7253 Top-5 准确率: 0.9320\n",
      "val 损失: 1.6877 Top-1 准确率: 0.5834 Top-5 准确率: 0.8416\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9365 Top-1 准确率: 0.7381 Top-5 准确率: 0.9387\n",
      "val 损失: 1.6046 Top-1 准确率: 0.6015 Top-5 准确率: 0.8565\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8999 Top-1 准确率: 0.7465 Top-5 准确率: 0.9428\n",
      "val 损失: 1.7125 Top-1 准确率: 0.5891 Top-5 准确率: 0.8472\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8773 Top-1 准确率: 0.7536 Top-5 准确率: 0.9454\n",
      "val 损失: 1.7197 Top-1 准确率: 0.5928 Top-5 准确率: 0.8499\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8236 Top-1 准确率: 0.7686 Top-5 准确率: 0.9506\n",
      "val 损失: 1.7476 Top-1 准确率: 0.5903 Top-5 准确率: 0.8459\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8077 Top-1 准确率: 0.7705 Top-5 准确率: 0.9523\n",
      "val 损失: 1.7306 Top-1 准确率: 0.5947 Top-5 准确率: 0.8499\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7655 Top-1 准确率: 0.7814 Top-5 准确率: 0.9577\n",
      "val 损失: 1.7034 Top-1 准确率: 0.6007 Top-5 准确率: 0.8559\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7412 Top-1 准确率: 0.7890 Top-5 准确率: 0.9594\n",
      "val 损失: 1.7444 Top-1 准确率: 0.6016 Top-5 准确率: 0.8522\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7234 Top-1 准确率: 0.7922 Top-5 准确率: 0.9613\n",
      "val 损失: 1.7051 Top-1 准确率: 0.6093 Top-5 准确率: 0.8568\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6873 Top-1 准确率: 0.8031 Top-5 准确率: 0.9656\n",
      "val 损失: 1.7119 Top-1 准确率: 0.6136 Top-5 准确率: 0.8592\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6697 Top-1 准确率: 0.8061 Top-5 准确率: 0.9666\n",
      "val 损失: 1.6856 Top-1 准确率: 0.6180 Top-5 准确率: 0.8620\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6454 Top-1 准确率: 0.8144 Top-5 准确率: 0.9682\n",
      "val 损失: 1.7074 Top-1 准确率: 0.6167 Top-5 准确率: 0.8630\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6350 Top-1 准确率: 0.8157 Top-5 准确率: 0.9698\n",
      "val 损失: 1.7859 Top-1 准确率: 0.6114 Top-5 准确率: 0.8590\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6070 Top-1 准确率: 0.8253 Top-5 准确率: 0.9715\n",
      "val 损失: 1.7589 Top-1 准确率: 0.6170 Top-5 准确率: 0.8591\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5892 Top-1 准确率: 0.8284 Top-5 准确率: 0.9732\n",
      "val 损失: 1.8104 Top-1 准确率: 0.6093 Top-5 准确率: 0.8537\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#loss_record, net = train(trainloader, net, optimizer, criterion, device, epoch_num = 10, save_model = False, testloader = testloader)\u001b[39;00m\n\u001b[1;32m      2\u001b[0m scheduler \u001b[38;5;241m=\u001b[39m lr_scheduler\u001b[38;5;241m.\u001b[39mStepLR(optimizer, step_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m50\u001b[39m, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m net, (train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtestloader\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtestloader\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/deep-learning/FuTong/chao_kk.py:111\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(trainloader, model, criterion, optimizer, device, scheduler, num_epochs, testloader)\u001b[0m\n\u001b[1;32m    108\u001b[0m         optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m    110\u001b[0m \u001b[38;5;66;03m# 统计\u001b[39;00m\n\u001b[0;32m--> 111\u001b[0m running_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m*\u001b[39m inputs\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    112\u001b[0m running_corrects \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m top1_correct\n\u001b[1;32m    113\u001b[0m running_top1_corrects \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m top1_correct\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#loss_record, net = train(trainloader, net, optimizer, criterion, device, epoch_num = 10, save_model = False, testloader = testloader)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "net, (train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(trainloader, net, criterion, optimizer, device, scheduler, num_epochs=50, testloader = testloader)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa99e052-c52d-49f3-b7a8-5c34679f4881",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10006667-359d-4be1-9dbb-18f04e71cbe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "056318ad-42d0-4684-99d8-9b989c20a0a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3237e3c6-473f-4799-b5da-403a2147382b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('NonLocal.pkl', 'wb') as f:\n",
    "    pickle.dump([\n",
    "        net, (train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs)\n",
    "    ], f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6515a9de-44af-4c74-8b34-8e1b7d514b86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6135\n",
      "Accuracy for class: apple is 84.0 %\n",
      "Accuracy for class: aquarium_fish is 76.0 %\n",
      "Accuracy for class: baby  is 45.0 %\n",
      "Accuracy for class: bear  is 33.0 %\n",
      "Accuracy for class: beaver is 33.0 %\n",
      "Accuracy for class: bed   is 62.0 %\n",
      "Accuracy for class: bee   is 61.0 %\n",
      "Accuracy for class: beetle is 48.0 %\n",
      "Accuracy for class: bicycle is 82.0 %\n",
      "Accuracy for class: bottle is 85.0 %\n",
      "Accuracy for class: bowl  is 43.0 %\n",
      "Accuracy for class: boy   is 37.0 %\n",
      "Accuracy for class: bridge is 58.0 %\n",
      "Accuracy for class: bus   is 32.0 %\n",
      "Accuracy for class: butterfly is 52.0 %\n",
      "Accuracy for class: camel is 54.0 %\n",
      "Accuracy for class: can   is 59.0 %\n",
      "Accuracy for class: castle is 85.0 %\n",
      "Accuracy for class: caterpillar is 63.0 %\n",
      "Accuracy for class: cattle is 59.0 %\n",
      "Accuracy for class: chair is 92.0 %\n",
      "Accuracy for class: chimpanzee is 78.0 %\n",
      "Accuracy for class: clock is 59.0 %\n",
      "Accuracy for class: cloud is 85.0 %\n",
      "Accuracy for class: cockroach is 69.0 %\n",
      "Accuracy for class: couch is 66.0 %\n",
      "Accuracy for class: crab  is 58.0 %\n",
      "Accuracy for class: crocodile is 52.0 %\n",
      "Accuracy for class: cup   is 81.0 %\n",
      "Accuracy for class: dinosaur is 56.0 %\n",
      "Accuracy for class: dolphin is 51.0 %\n",
      "Accuracy for class: elephant is 64.0 %\n",
      "Accuracy for class: flatfish is 54.0 %\n",
      "Accuracy for class: forest is 78.0 %\n",
      "Accuracy for class: fox   is 60.0 %\n",
      "Accuracy for class: girl  is 32.0 %\n",
      "Accuracy for class: hamster is 81.0 %\n",
      "Accuracy for class: house is 64.0 %\n",
      "Accuracy for class: kangaroo is 44.0 %\n",
      "Accuracy for class: keyboard is 75.0 %\n",
      "Accuracy for class: lamp  is 57.0 %\n",
      "Accuracy for class: lawn_mower is 83.0 %\n",
      "Accuracy for class: leopard is 64.0 %\n",
      "Accuracy for class: lion  is 73.0 %\n",
      "Accuracy for class: lizard is 37.0 %\n",
      "Accuracy for class: lobster is 50.0 %\n",
      "Accuracy for class: man   is 43.0 %\n",
      "Accuracy for class: maple_tree is 65.0 %\n",
      "Accuracy for class: motorcycle is 78.0 %\n",
      "Accuracy for class: mountain is 80.0 %\n",
      "Accuracy for class: mouse is 43.0 %\n",
      "Accuracy for class: mushroom is 66.0 %\n",
      "Accuracy for class: oak_tree is 33.0 %\n",
      "Accuracy for class: orange is 91.0 %\n",
      "Accuracy for class: orchid is 75.0 %\n",
      "Accuracy for class: otter is 28.0 %\n",
      "Accuracy for class: palm_tree is 76.0 %\n",
      "Accuracy for class: pear  is 72.0 %\n",
      "Accuracy for class: pickup_truck is 63.0 %\n",
      "Accuracy for class: pine_tree is 46.0 %\n",
      "Accuracy for class: plain is 71.0 %\n",
      "Accuracy for class: plate is 85.0 %\n",
      "Accuracy for class: poppy is 67.0 %\n",
      "Accuracy for class: porcupine is 61.0 %\n",
      "Accuracy for class: possum is 29.0 %\n",
      "Accuracy for class: rabbit is 51.0 %\n",
      "Accuracy for class: raccoon is 50.0 %\n",
      "Accuracy for class: ray   is 61.0 %\n",
      "Accuracy for class: road  is 88.0 %\n",
      "Accuracy for class: rocket is 84.0 %\n",
      "Accuracy for class: rose  is 53.0 %\n",
      "Accuracy for class: sea   is 80.0 %\n",
      "Accuracy for class: seal  is 15.0 %\n",
      "Accuracy for class: shark is 35.0 %\n",
      "Accuracy for class: shrew is 40.0 %\n",
      "Accuracy for class: skunk is 68.0 %\n",
      "Accuracy for class: skyscraper is 78.0 %\n",
      "Accuracy for class: snail is 54.0 %\n",
      "Accuracy for class: snake is 54.0 %\n",
      "Accuracy for class: spider is 73.0 %\n",
      "Accuracy for class: squirrel is 54.0 %\n",
      "Accuracy for class: streetcar is 82.0 %\n",
      "Accuracy for class: sunflower is 90.0 %\n",
      "Accuracy for class: sweet_pepper is 44.0 %\n",
      "Accuracy for class: table is 62.0 %\n",
      "Accuracy for class: tank  is 81.0 %\n",
      "Accuracy for class: telephone is 65.0 %\n",
      "Accuracy for class: television is 73.0 %\n",
      "Accuracy for class: tiger is 70.0 %\n",
      "Accuracy for class: tractor is 70.0 %\n",
      "Accuracy for class: train is 55.0 %\n",
      "Accuracy for class: trout is 75.0 %\n",
      "Accuracy for class: tulip is 39.0 %\n",
      "Accuracy for class: turtle is 31.0 %\n",
      "Accuracy for class: wardrobe is 94.0 %\n",
      "Accuracy for class: whale is 45.0 %\n",
      "Accuracy for class: willow_tree is 50.0 %\n",
      "Accuracy for class: wolf  is 75.0 %\n",
      "Accuracy for class: woman is 35.0 %\n",
      "Accuracy for class: worm  is 75.0 %\n"
     ]
    }
   ],
   "source": [
    "show_all_class_acc(classes, testloader, net, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37015137-6e99-4720-aaa0-1146bb2f4e09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3e6bcd8-be18-43cf-afe4-4a35930a24cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResBlock(nn.Module):\n",
    "  def __init__(self, in_channel, out_channel, stride, downsample = None, dilation = 1):\n",
    "    super(ResBlock, self).__init__()\n",
    "    self.conv1 = conv3x3(in_channel, out_channel, stride, dilation)\n",
    "    self.bn1 = nn.BatchNorm2d(out_channel)\n",
    "    self.conv2 = conv3x3(out_channel, out_channel, 1, dilation)\n",
    "    self.bn2 = nn.BatchNorm2d(out_channel)\n",
    "    self.relu = nn.ReLU()\n",
    "    self.downsample = downsample\n",
    "\n",
    "  def forward(self, x):\n",
    "    shortcut = x\n",
    "    x = self.conv1(x)\n",
    "    x = self.bn1(x)\n",
    "    x = self.relu(x)\n",
    "    x = self.conv2(x)\n",
    "    x = self.bn2(x)\n",
    "    if self.downsample != None:\n",
    "      shortcut = self.downsample(shortcut)\n",
    "    x += shortcut\n",
    "    return x\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "  def __init__(self, classes, base_channel = 64, embed_dim=512, num_heads=8, num_layers=6):\n",
    "    super(ResNet, self).__init__()\n",
    "    # 老实说，我怕64、128……512通道实在是太慢了，通过base_channel来调节通道。感觉可以设置为16\n",
    "    self.base_channel = base_channel\n",
    "    self.conv1 = conv3x3(3, base_channel, 1)\n",
    "    self.bn = nn.BatchNorm2d(base_channel)\n",
    "    self.relu = nn.ReLU()\n",
    "    self.max_pool = nn.MaxPool2d(kernel_size = 2, stride = 2)\n",
    "    self.conv2_x = self.init_ResBlock(base_channel * 1, base_channel * 1, stride = 1, block_num = 2, dilation = 1)\n",
    "    self.conv3_x = self.init_ResBlock(base_channel * 1, base_channel * 2, stride = 2, block_num = 2, dilation = 1)\n",
    "    self.conv4_x = self.init_ResBlock(base_channel * 2, base_channel * 4, stride = 2, block_num = 2, dilation = 1)\n",
    "    self.conv5_x = self.init_ResBlock(base_channel * 4, base_channel * 8, stride = 2, block_num = 2, dilation = 1)\n",
    "\n",
    "    embed_dim = base_channel * 8\n",
    "    self.transformer = TransformerEncoder(embed_dim, num_heads, num_layers)\n",
    "    self.fc1 = nn.Linear(embed_dim, embed_dim // 2)\n",
    "    self.fc2 = nn.Linear(embed_dim // 2, embed_dim // 4)\n",
    "    self.fc3 = nn.Linear(embed_dim // 4, len(classes))\n",
    "    self.pool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "    self.dropout = nn.Dropout(0.3)\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.conv1(x)\n",
    "    x = self.bn(x)\n",
    "    x = self.relu(x)\n",
    "    x = self.conv2_x(x)\n",
    "    x = self.conv3_x(x)\n",
    "    x = self.conv4_x(x)\n",
    "    x = self.conv5_x(x)\n",
    "\n",
    "    #print(x.shape) torch.Size([32, 512, 4, 4])\n",
    "    x = self.pool(x)  # shape: (batch_size, embed_dim, 1, 1)\n",
    "    x = x.squeeze(2).squeeze(2)  # shape: (batch_size, embed_dim)\n",
    "    # Prepare data for Transformer: (sequence_length, batch_size, embed_dim)\n",
    "    x = x.unsqueeze(0)  # shape: (1, batch_size, embed_dim)\n",
    "    # Transformer Encoder\n",
    "    x = self.transformer(x)  # shape: (sequence_length, batch_size, embed_dim)\n",
    "    # Remove the sequence dimension\n",
    "    x = x.squeeze(0)  # shape: (batch_size, embed_dim)\n",
    "    # Final classification layer\n",
    "    x = self.fc1(x) \n",
    "    x = self.relu(x)\n",
    "    x = self.fc2(x) \n",
    "    x = self.relu(x)\n",
    "    x = self.fc3(x) \n",
    "    x = self.dropout(x)\n",
    "    return x\n",
    "\n",
    "  def init_ResBlock(self, in_channel, out_channel, stride, block_num, dilation):\n",
    "    downsample = None #这个好东西是用来把特征图宽高缩小的\n",
    "    if stride != 1 or in_channel != out_channel:\n",
    "      # 对于3*3的padding为1的卷积核，如果stride为1的话卷完后特征图宽高是不变的。\n",
    "      # 如果stride是2的话，那么宽高会变成原来的一半。\n",
    "      # 计算：out = (in/2 - 1/2)向下取整 + 1， 也就是in/2 - 1 + 1 = in / 2\n",
    "      downsample = nn.Sequential(\n",
    "                conv3x3(in_channel, out_channel, stride=stride),\n",
    "                nn.BatchNorm2d(out_channel)\n",
    "                )\n",
    "    return nn.Sequential(*[\n",
    "        ResBlock(in_channel, out_channel, stride, downsample = downsample, dilation = dilation) if i == 0 else\n",
    "        ResBlock(out_channel, out_channel, 1, dilation = dilation)\n",
    "        for i in range(block_num)\n",
    "    ])\n",
    "\n"
   ]
  }
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